AI-901 Practice Exam.
Free practice test — 36 verified questions, instant feedback.

Answer: A
Counting objects in images is an object detection task, which falls under computer vision. Natural language processing deals with text and speech, not images; knowledge mining is about extracting structured information from large document repositories; generative AI creates new content rather than analyzing existing images for object counts.

Answer: A
Understanding and summarizing free-text human language, and classifying its intent for routing, are classic natural language processing tasks. Computer vision applies to images, anomaly detection identifies unusual data patterns, and reinforcement learning trains agents through trial-and-error rewards rather than text understanding.

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Fairness means an AI system's decisions or predictions should not disadvantage or discriminate against particular groups based on characteristics such as gender, ethnicity, or age. Deterministic output is a reproducibility property, not fairness; being open source relates to transparency-adjacent practices, not fairness itself; and deliberately excluding demographic groups is the opposite of fairness.

Answer: A
A model producing systematically different outcomes correlated with a protected or proxy attribute (like zip code standing in for demographics) is a fairness problem, even if the sensitive feature was removed, because proxy variables can still encode bias. Inclusiveness concerns designing for people with diverse abilities and needs; reliability and safety concerns consistent, safe operation under expected conditions; privacy and security concerns data protection, not biased outcomes.

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Transparency in responsible AI is about making a system's behavior understandable and communicating its capabilities, limitations, and decision rationale to stakeholders. It does not require publishing source code, has nothing to do with physical data center design, and is unrelated to encryption practices, which fall under security.

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Predicting a continuous numeric value, like price, is a regression task. Classification predicts a discrete category or label rather than a continuous number; clustering groups similar unlabeled data points without predicting a target value; reinforcement learning trains an agent through rewards and penalties over time, not by fitting a single continuous output.

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Clustering is an unsupervised technique that groups unlabeled data points based on similarity, which fits discovering unknown customer segments. Classification and supervised learning with labeled outcomes both require predefined labels, which don't exist here; regression predicts a continuous numeric value rather than discovering groupings.

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Generative models like LLMs produce new content (text, code, etc.) by predicting likely continuations based on patterns learned from vast training data, whereas classification models output a label from a fixed, predefined set of categories. LLMs are trained on massive text corpora, not 'no data'; they typically process text (and increasingly other modalities), not just numbers; and the two model types serve fundamentally different purposes, not just different names.

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A prompt is the natural-language input or instruction given to a generative model to elicit a desired response; prompt design and phrasing directly influence output quality. It is not a compiled configuration file, not a neural network weight, and not a piece of training hardware.

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Agentic AI extends generative models with the ability to reason over a goal, break it into steps, and autonomously call tools or APIs to complete tasks, rather than just responding to a single prompt. It is not limited to voice input, it can access external data sources (often through connected tools), and speed or accuracy depends on implementation, not an inherent property of being agentic.

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Automatically detecting sentiment and routing based on inferred meaning requires a trained model interpreting unstructured text, which is a natural language processing AI workload. File copying on a schedule, a static webpage, and a spreadsheet sum are all deterministic, rule-based operations with no learning or inference involved.

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The model catalog in Microsoft Foundry gives developers a searchable collection of foundation models (from Microsoft and partners) with details on capabilities and benchmarks, so they can select the right model for their scenario. It is not a billing tool, a staffing directory, or a general-purpose VM size list, which are unrelated Azure concerns.

Answer: A
Microsoft Foundry provides agent-building and orchestration features that let a language model call tools/APIs and maintain state across a multi-step interaction, enabling agentic applications. Thumbnail generation is unrelated to conversational orchestration, manual SQL execution bypasses model involvement entirely, and a static spreadsheet template has no dynamic model or tool-calling capability.

Answer: A
Converting spoken audio into written text is the function of speech-to-text (speech recognition) services available through Microsoft Foundry's speech capabilities. Text-to-image generation creates pictures from text prompts, not transcripts; OCR extracts text from images of documents, not live audio; and anomaly detection identifies unusual patterns in numeric time-series data, unrelated to transcription.

Answer: A
Text-to-speech synthesis converts written text into spoken audio output, which is exactly what's needed to read content aloud. Speech-to-text does the reverse (audio to text); named entity recognition extracts entities like names and dates from text, it doesn't produce audio; and image captioning describes images in text, unrelated to reading text aloud.

Answer: A
Extracting structured fields like vendor name, totals, and dates from scanned documents is the core purpose of document intelligence and information extraction capabilities, which combine OCR with field-level understanding. Text-to-speech produces audio, not structured data; image generation creates new pictures rather than reading existing ones; sentiment analysis judges emotional tone in text, not extracting invoice fields.

Answer: A
Generating new images from text descriptions is precisely what text-to-image generation models provide, letting teams create original visuals without traditional photography. OCR reads text out of existing images rather than creating new ones, speech translation converts spoken language between languages, and key phrase extraction pulls important terms out of text, none of which produce images.

Answer: A
Object detection identifies multiple objects within an image and returns their locations as bounding boxes, matching the need to locate cars, pedestrians, and signs individually. Image classification assigns a single label to the whole image without locating objects; OCR reads text characters, not general objects; and image captioning produces a single descriptive sentence rather than bounding boxes for each object.

Answer: A
Semantic segmentation classifies every pixel in an image into a category, producing a pixel-level map, which fits labeling roads, buildings, vegetation, and water across an aerial photo. Image classification only assigns one label to the whole image, not per pixel; OCR is for reading text characters; facial recognition identifies human faces, irrelevant to land-cover mapping.

Answer: A
Optical character recognition is designed specifically to detect and extract printed or handwritten text from images and convert it into machine-readable text. Sentiment analysis judges emotional tone in text that already exists digitally, speech synthesis converts text to audio, and anomaly detection flags unusual data patterns, none of which extract text from images.

Answer: A
Sentiment analysis specifically evaluates text to determine the emotional tone, such as positive, negative, or neutral. Named entity recognition extracts entities like names, places, and organizations rather than tone; language translation converts text between languages; text-to-speech converts text into spoken audio, none of which assess opinion polarity.

Answer: A
Named entity recognition identifies and categorizes specific entities in text, such as people, organizations, dates, and monetary values, exactly matching the contract-tagging requirement. Sentiment analysis measures opinion polarity, not entity extraction; language detection identifies which language text is written in; and speech-to-text converts audio to text, unrelated to tagging entities within existing text.

Answer: A
Machine translation converts text from one language to another, which is exactly what's needed to turn French messages into English. Key phrase extraction pulls out important terms but doesn't translate language; text summarization condenses content length rather than changing language; speech synthesis converts text into audio, not between languages.

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Text summarization condenses longer text into a shorter version while retaining key meaning, which fits generating brief article previews. Named entity recognition extracts entities rather than condensing text; object detection and facial recognition are computer vision tasks applied to images, unrelated to summarizing written articles.

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When a generative model produces plausible-sounding but factually incorrect or fabricated content, it's called hallucination, and grounding techniques like retrieval-augmented generation, which supply verified source documents at inference time, help reduce it. Overfitting and underfitting are training-time model-fit problems unrelated to runtime factual errors, learning rate is a training hyperparameter not adjustable at inference, and latency concerns response speed, not factual accuracy.

Answer: A
Retrieval-augmented generation retrieves relevant passages from an external knowledge source and supplies them as context to a language model, grounding its answers in company-specific data rather than only pretrained knowledge. Training reinforcement learning from raw pixels is unrelated to text Q&A, a rule-based decision tree bypasses the language model entirely, and image classification categorizes images rather than answering knowledge questions.

Answer: A
Temperature controls the randomness of token selection during generation; lowering it makes the model favor higher-probability tokens, producing more consistent, focused output, while raising it increases variety and creativity. It does not cause the model to ignore prompts, does not directly control response latency, and does not change the underlying model architecture, which is a separate deployment choice.

Answer: A
Smaller models generally cost less to run and respond faster, making them well suited to simpler tasks like FAQ answering where the full reasoning power of a large frontier model isn't required. Creativity and originality are not inherently tied to model size in the smaller model's favor, prompt engineering is still valuable regardless of model size, and smaller models typically support shorter, not unlimited, context windows compared to larger frontier models.

Answer: A
Automated filters that detect and block categories of harmful content, such as hate speech or self-harm content, before it reaches users are content safety and harm mitigation controls, a key responsible AI implementation practice. Encryption at rest protects stored data, autoscaling manages compute capacity under load, and version rollback reverts to a prior model deployment, none of which filter harmful output content.

Answer: A
Reliability and safety concerns an AI system performing consistently and safely, including under less-than-ideal conditions such as noisy input, without unexpected failures. Inclusiveness is about empowering people of diverse abilities and backgrounds, accountability concerns having clear human responsibility for AI system outcomes, and privacy and security concerns protecting data, none of which specifically address robustness to noisy input.

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Accountability means people and organizations remain responsible for how an AI system operates and are able to govern, audit, and correct its behavior. Deploying without human oversight, hiding internal logic from regulators, and deleting training data all reduce oversight and traceability, working against accountability rather than demonstrating it.

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Inclusiveness means designing AI systems to bring benefits to and work well for people of all backgrounds, abilities, and characteristics, which matches supporting varied accents and speech patterns. Transparency is about explaining how the system works, privacy and security is about protecting data, and reliability and safety is about consistent safe operation, none of which specifically target designing for diverse user populations.

Answer: A
Microsoft certification exams such as AI-901 are scheduled through the exam delivery provider Pearson VUE from the exam or certification details page. Prometric is a different, unrelated testing vendor used by other certification programs; the exam is not exclusively ProctorU-only since a local test center option also exists; and employer HR departments do not schedule Microsoft certification exams.

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The passing score for AI-901 is 700 or greater on the scaled reporting range. The other values are incorrect thresholds not used for this exam's passing bar.

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Candidates can retake the exam 24 hours after their first attempt. Longer waiting periods like 7 or 30 days do not apply to the first retake, and there is in fact a mandatory minimum wait rather than immediate retake eligibility.

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Candidates whose preferred language is unavailable can request an additional 30 minutes to complete the exam. Shorter or longer time allowances listed are not the documented accommodation, and additional time is in fact available under this specific circumstance.